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OBJECTIVE To explore the potential value of heart rate variability features for automated monitoring of sedation levels in mechanically ventilated ICU patients. DESIGN Multicenter, pilot study. SETTING Several ICUs at Massachusetts General Hospital, Boston, MA. PATIENTS Electrocardiogram recordings from 40 mechanically ventilated adult patients(More)
Atomic decomposition (AD) can be used to efficiently decompose an arbitrary signal. In this paper, we present a method to detect neonatal electroencephalogram (EEG) seizure based on AD via orthogonal matching pursuit using a novel, application-specific, dictionary. The dictionary consists of pseudoperiodic Duffing oscillator atoms which are designed to be(More)
The development of automated methods of electroencephalogram (EEG) seizure detection is an important problem in neonatology. This paper proposes improvements to a previously described method of seizure detection based on atomic decomposition by developing a new time-frequency (TF) dictionary that is highly coherent with the newborn EEG seizure. We compare(More)
We developed a simple and fully automated method for detecting artifacts in the R-R interval (RRI) time series of the ECG that is tailored to the intensive care unit (ICU) setting. From ECG recordings of 50 adult ICU-subjects we selected 60 epochs with valid R-peak detections and 60 epochs containing artifacts leading to missed or false positive R-peak(More)
OBJECTIVE To develop a personalizable algorithm to discriminate between sedation levels in ICU patients based on heart rate variability. DESIGN Multicenter, pilot study. SETTING Several ICUs at Massachusetts General Hospital, Boston, MA. PATIENTS We gathered 21,912 hours of routine electrocardiogram recordings from a heterogenous group of 70 adult ICU(More)
An automated patient-specific system to classify the level of sedation in ICU patients using heart rate variability signal is presented in this paper. ECG from 70 mechanically ventilated adult patients with administered sedatives in an ICU setting were used to develop a support vector machine based system for sedation depth monitoring using several heart(More)
A method for the design of nearly linear-phase recursive digital filters is proposed. The recursive filter is assumed be a cascade arrangement of second-order biquadratic sections whose transfer functions are expressed in the polar form. An error function is formulated based on the difference between the actual complex frequency response of the filter and(More)
Aim: To develop an automated system to monitor sedation levels in intensive care unit patients using heart rate variability (HRV). Methods: We developed an automatic sedation level prediction system using HRV as input to a support vector machine learning algorithm. Our data consisted of electrocardiogram recordings from a heterogeneous group of 50(More)
In this paper we examined the robustness of a feature-set based on time-frequency distributions (TFDs) for neonatal EEG seizure detection. This feature-set was originally proposed in literature for neonatal seizure detection using a support vector machine (SVM). We tested the performance of this feature-set with a smoothed Wigner-Ville distribution and(More)